Video Analysis of Newborn Resuscitations After Simulation-Based Helping Babies Breathe Training

Background Simulation-based Helping Babies Breathe (HBB) training is currently rolled-out in around 80 low-income countries with various results. Method Workflow was analyzed in 76 video-recorded newborn resuscitations performed by regularly HBB-trained nurse-midwives over 3 years in rural Tanzania. Results Actual newborn resuscitation practice deviated from HBB intention/guideline: most newborns underwent prolonged suction and stimulation before ventilation; ventilation was delayed and frequently interrupted. Nurse-midwives often worked together. Conclusions There is a gap between training intention and clinical practice. HBB trainings should focus more on urgency, ventilation skills, and team training. Combining clinical debriefing with HBB simulations could facilitate continuous learning and application.publishedVersio

Video Analysis of Newborn Resuscitations After Simulation-Based Helping Babies Breathe Training

Background: Simulation-based Helping Babies Breathe (HBB) training is currently rolled-out in around 80 low-income countries with various results. Method: Workflow was analyzed in 76 video-recorded newborn resuscitations performed by regularly HBB-trained nurse-midwives over 3 years in rural Tanzania. Results: Actual newborn resuscitation practice deviated from HBB intention/guideline: most newborns underwent prolonged suction and stimulation before ventilation; ventilation was delayed and frequently interrupted. Nurse-midwives often worked together. Conclusions:There is a gap between training intention and clinical practice. HBB trainings should focus more on urgency, ventilation skills, and team training. Combining clinical debriefing with HBB simulations could facilitate continuous learning and applicatio

Neonatal Resuscitation Skill-Training Using a New Neonatal Simulator, Facilitated by Local Motivators: Two-Year Prospective Observational Study of 9000 Trainings

Globally, intrapartum-related complications account for approximately 2 million perinatal deaths annually. Adequate skills in neonatal resuscitation are required to reduce perinatal mortality. NeoNatalie Live is a newborn simulator providing immediate feedback, originally designed to accomplish Helping Babies Breathe training in low-resource settings. The objectives of this study were to describe changes in staff participation, skill-training frequency, and simulated ventilation quality before and after the introduction of “local motivators” in a rural Tanzanian hospital with 4000–5000 deliveries annually. Midwives (n = 15–27) were encouraged to perform in situ low-dose high-frequency simulation skill-training using NeoNatalie Live from September 2016 through to August 2018. Frequency and quality of trainings were automatically recorded in the simulator. The number of skill-trainings increased from 688 (12 months) to 8451 (11 months) after the introduction of local motivators in October 2017. Staff participation increased from 43% to 74% of the midwives. The quality of training performance, measured as “well done” feedback, increased from 75% to 91%. We conclude that training frequency, participation, and performance increased after introduction of dedicated motivators. In addition, the immediate constructive feedback features of the simulator may have influenced motivation and training quality performance.publishedVersio

Children save lives: evaluation of a first aid training in Norwegian kindergartens

Part of effective Early Childhood Education and Care is to support children’s awareness of hazards and management of possible injuries during risky play. This study evaluated the attitudes of Norwegian kindergarten teachers towards “Henry first aid training” and its impact on 3-6-year olds’ understanding of first aid. 588 kindergarten employees completed an online survey and 50 children (26 boys and 24 girls) participated in semi-structured interviews gauging their knowledge of first aid before and after using Henry. These children’s knowledge of first aid was compared to 46 children who had used Henry over a longer period. Survey results indicated strong enthusiasm among kindergarten teachers for the use of Henry. Children’s understanding of first aid increased from pre- to post- interviews, t (43) = 8.878, p < 0 .001, Cohen’s d = 1.32). Findings are discussed in relation to international scalability of Henry and the need for training kindergarten teachers and children in first aid.publishedVersio

Automatic identification of stimulation activities during newborn resuscitation using ECG and accelerometer signals

Background and Objective: Early neonatal death is a worldwide challenge with 1 million newborn deaths every year. The primary cause of these deaths are complications during labour and birth asphyxia. The majority of these newborns could have been saved with adequate resuscitation at birth. Newborn resuscitation guidelines recommend immediate drying, stimulation, suctioning if indicated, and ventilation of non-breathing newborns. A system that will automatically detect and extract time periods where different resuscitation activities are performed, would be highly beneficial to evaluate what resuscitation activities that are improving the state of the newborn, and if current guidelines are good and if they are followed. The potential effects of especially stimulation are not very well documented as it has been difficult to investigate through observations. In this paper the main objective is to identify stimulation activities, regardless if the state of the newborn is changed or not, and produce timelines of the resuscitation episode with the identified stimulations. Methods: Data is collected by utilizing a new heart rate device, NeoBeat, with dry-electrode ECG and accelerometer sensors placed on the abdomen of the newborn. We propose a method, NBstim, based on time domain and frequency domain features from the accelerometer signals and ECG signals from NeoBeat, to detect time periods of stimulation. NBstim use causal features from a gliding window of the signals, thus it can potentially be used in future realtime systems. A high performing feature subset is found using feature selection. System performance is computed using a leave-one-out cross-validation and compared with manual annotations. Results: The system achieves an overall accuracy of 90.3% when identifying regions with stimulation activities. Conclusion: The performance indicates that the proposed NBstim, used with signals from the NeoBeat can be used to determine when stimulation is performed. The provided activity timelines, in combination with the status of the newborn, for example the heart rate, at different time points, can be studied further to investigate both the time spent and the effect of different newborn resuscitation parameter

Automatic prediction of therapeutic activities during newborn resuscitation combining video and signal data

Newborn mortality is a global challenge with around 2.4 million neonatal deaths in 2019. One third of these occur within the first-and-only day of life with labour complications and birth asphyxia being the primary causes. Existing guidelines for newborn resuscitation are based on limited scientific evidence, and evidens based research is sought for. To increase our knowledge on resuscitation of newborns, it is crucial to first quantify what is currently being done in terms of therapeutic activities, such as ventilation and stimulation, and how they affect resuscitation outcomes. In the current study, the therapeutic activities during newborn resuscitation are quantified by estimating a timeline describing the start and stop of activities. The proposed approach is combining methods using both video and time series data recorded during resuscitation, where the predictions are based on the available sources. From video the activity recognition is done by a 3D CNN method. For the signal data feature extraction is performed on ECG and accelerometer signals and thereafter machine learning is done to perform stimulation detection. We show that best results are achieved with all signals and video available, for the activity ‘‘stimulation’’ we get an AUC of 0.86, sensitivity of 82.32%, specificity of 82.23%, and precision of 57.59%. If only signals or video is available we still get good results with AUC at 0.80, and 0.84 respectivel

Neonatal Resuscitation Skill-Training Using a New Neonatal Simulator, Facilitated by Local Motivators: Two-Year Prospective Observational Study of 9000 Trainings

Globally, intrapartum-related complications account for approximately 2 million perinatal deaths annually. Adequate skills in neonatal resuscitation are required to reduce perinatal mortality. NeoNatalie Live is a newborn simulator providing immediate feedback, originally designed to accomplish Helping Babies Breathe training in low-resource settings. The objectives of this study were to describe changes in staff participation, skill-training frequency, and simulated ventilation quality before and after the introduction of “local motivators” in a rural Tanzanian hospital with 4000–5000 deliveries annually. Midwives (n = 15–27) were encouraged to perform in situ low-dose high-frequency simulation skill-training using NeoNatalie Live from September 2016 through to August 2018. Frequency and quality of trainings were automatically recorded in the simulator. The number of skill-trainings increased from 688 (12 months) to 8451 (11 months) after the introduction of local motivators in October 2017. Staff participation increased from 43% to 74% of the midwives. The quality of training performance, measured as “well done” feedback, increased from 75% to 91%. We conclude that training frequency, participation, and performance increased after introduction of dedicated motivators. In addition, the immediate constructive feedback features of the simulator may have influenced motivation and training quality performance